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Field
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superstructure combining cycles from the literature and cycles generated by AI models. -- Use of process representation formalisms (graphs, SFILES) and process synthesis tools. - Solving the “Product Design
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 3 months ago
new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and predict food-effector
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molecular level. To yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and
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. Develop the Core AI Predictor - You will explore and train advanced models, such as Graph Neural Networks (GNNs), to solve the key challenge: distinguishing benign aging from true failure precursors
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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dataDesigning hierarchical graph‑based models to predict toxicity under uncertainty by linking molecular‑level and system‑level knowledgeAdvancing causal inference methods to predict transformation products under
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build on ongoing developments in graph-based analysis of point cloud data and explore structured approaches to bone-wise alignment and rigid transformation estimation. The project combines methodological
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promising tools for addressing these challenges. Large language models can help bridge communication gaps between subject experts, while knowledge graphs can capture complex semantic relationships and provide
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 3 months ago
the form of graphs to analyze and predict food-effector systems. Key Responsibilities Develop Probabilistic Machine Learning Models to integrate graphs and food-related omics data Multi-omics integration